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Evolving two-dimensional fuzzy systems
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DOI：10.1016/S0165-0114(02)00483-9.png)
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Abstract
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The design of fuzzy logic systems (FLS) generally involves determining the structure of the rules and the parameters of the membership functions. In this paper we present a methodology based on evolutionary computation for simultaneously designing membership functions and appropriate rule sets. This property makes it different from many techniques that address these goals separately with the result of suboptimal solutions because the design elements are mutually dependent. We also apply a hew approach in which the evolutionary algorithm is applied directly to a FLS data structure instead of a binary or other codification. Results on function approximation show improvements over other incremental and analytical methods. (C) 2002 Elsevier B.V. All rights reserved.
Keywords:
fuzzy systems
genetic algorithms
evolutionary algorithms
hybrid methods
function approximation
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